




"""simple docstring"""

def 		_snake_case	(   _snake_case				:   str				)       ->   str:






      '''simple docstring'''


      _A     =  0

      # if input_string is "aba" than new_input_string become "a|b|a"
      _A     =  ''
      _A     =  ''

      # append each character + "|" in new_string for range(0, length-1)
      for i in input_string[: len(_snake_case				) - 1]:
            new_input_string += i + "|"
      # append last character
      new_input_string += input_string[-1]

      # we will store the starting and ending of previous furthest ending palindromic
      # substring
      _A  ,       _A     =  0, 0

      # length[i] shows the length of palindromic substring with center i
      _A     =  [1 for i in range(len(_snake_case				)				)]

      # for each character in new_string find corresponding palindromic string
      _A     =  0
      for j in range(len(_snake_case				)				):
            _A     =  1 if j > r else min(length[l + r - j] // 2							,    r - j + 1				)
            while (
                j - k >= 0
                and j + k < len(_snake_case				)
                and new_input_string[k + j] == new_input_string[j - k]
            ):
                  k += 1

            _A     =  2 * k - 1

            # does this string is ending after the previously explored end (that is r) ?
            # if yes the update the new r to the last index of this
            if j + k - 1 > r:
                  _A     =  j - k + 1  # noqa: E741
                  _A     =  j + k - 1

            # update max_length and start position
            if max_length < length[j]:
                  _A     =  length[j]
                  _A     =  j

    # create that string
      _A     =  new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
      for i in s:
            if i != "|":
                  output_string += i

      return output_string


if __name__ == "__main__":
       import doctest

       doctest.testmod()


from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					UNetaDModel
			__a    :					ScoreSdeVeScheduler





			def __init__(  self ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   Union[str, Any]:

							'''simple docstring'''


							super().__init__()
							self.register_modules(unet=lowerCAmelCase__ ,  scheduler=lowerCAmelCase__		)





			@torch.no_grad()
			def __call__(  self ,  lowerCAmelCase__ = 1 ,  lowerCAmelCase__ = 20_00 ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = "pil" ,  lowerCAmelCase__ = True ,  **lowerCAmelCase__ ,  )	->   Union[ImagePipelineOutput, Tuple]:

							'''simple docstring'''


							__lowercase					     =				self.unet.config.sample_size
							__lowercase					     =				(batch_size, 3, img_size, img_size)

							__lowercase					     =				self.unet

							__lowercase					     =				randn_tensor(lowerCAmelCase__ ,  generator=lowerCAmelCase__		) * self.scheduler.init_noise_sigma
							__lowercase					     =				sample.to(self.device		)

							self.scheduler.set_timesteps(lowerCAmelCase__		)
							self.scheduler.set_sigmas(lowerCAmelCase__		)

							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
											__lowercase					     =				self.scheduler.sigmas[i] * torch.ones(shape[0] ,  device=self.device		)

											# correction step
											for _ in range(self.scheduler.config.correct_steps		):
															__lowercase					     =				self.unet(lowerCAmelCase__ ,  lowerCAmelCase__		).sample
															__lowercase					     =				self.scheduler.step_correct(lowerCAmelCase__ ,  lowerCAmelCase__ ,  generator=lowerCAmelCase__		).prev_sample

											# prediction step
											__lowercase					     =				model(lowerCAmelCase__ ,  lowerCAmelCase__		).sample
											__lowercase					     =				self.scheduler.step_pred(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  generator=lowerCAmelCase__		)

											__lowercase     ,	__lowercase					     =				output.prev_sample, output.prev_sample_mean

							__lowercase					     =				sample_mean.clamp(0 ,  1		)
							__lowercase					     =				sample.cpu().permute(0 ,  2 ,  3 ,  1		).numpy()
							if output_type == "pil":
											__lowercase					     =				self.numpy_to_pil(lowerCAmelCase__		)

							if not return_dict:
											return (sample,)

							return ImagePipelineOutput(images=lowerCAmelCase__		)